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This paper explains how Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken dialogue systems. It briefly summarises the basic mathematics and explains why exact optimisation is intractable. It then describes in some detail a form of approximation called the(More)
Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a(More)
Modelling dialogue as a Partially Observable Markov Decision Process (POMDP) enables a dialogue policy robust to speech understanding errors to be learnt. However , a major challenge in POMDP policy learning is to maintain tractability, so the use of approximation is inevitable. We propose applying Gaussian Processes in Reinforcement learning of optimal(More)
In any dialogue manager, confidence scores play a central role in ensuring robust operation. Recently, dialogue managers have attempted to exploit N-best lists of alternatives for the semantics rather than the single most likely interpretation. Each alternative in the N-best list must have an associated confidence score and it is very useful to be able to(More)
The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multilingual dialogue systems intractable. Moreover, human languages are context-aware. The most(More)
Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method(More)
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usabil-ity and perceived quality. Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. They are also not easily scaled to systems(More)
While data-driven methods for spoken language understanding reduce maintenance and portability costs compared with handcrafted parsers, the collection of word-level semantic annotations for training remains a time-consuming task. A recent line of research has focused on building generative models from unaligned semantic representations , using(More)
HMM based synthesis has attracted great interest due to its compact and flexible modelling of spectral and prosodic parameters. In this approach, short term spectra, fundamental frequency (F0) and duration are simultaneously modelled by multi-stream HMMs. However , since F0 values in unvoiced regions are normally considered as undefined, it is difficult to(More)